Control of sexual stimulation devices using electroencephalography

ABSTRACT

A system and method for thought-based control of sexual stimulation devices using electroencephalography. The system and method involve a training phase and an operation phase. In an embodiment, the training phase comprises placing electrodes on the head of a person, measuring electrical signals produced by the person&#39;s brain via the electrodes while the user engages in one or more thought-based training tasks, associating patterns of electrical activity in the person&#39;s brain while performing the tasks with controls of the sexual stimulation device. In an embodiment, the operation phase comprises generating control signals for the sexual stimulation device based on the associations when the patterns of electrical activity are detected by the electrodes. In some embodiments, machine learning algorithms are used to detect the patterns of electrical activity and make the associations. In some embodiments, data from other biometric sensors is included in the associations.

CROSS-REFERENCE TO RELATED APPLICATIONS

Priority is claimed in the application data sheet to the followingpatents or patent applications, the entire written description of eachof which is expressly incorporated herein by reference in its entirety:

-   -   Ser. No. 17/737,974    -   Ser. No. 17/534,155    -   Ser. No. 16/861,014    -   Ser. No. 16/214,030    -   Ser. No. 16/139,550

BACKGROUND Field of the Art

The present invention is in the field of computer control systems, andmore specifically the field of control systems for sexual stimulationdevices.

Discussion of the State of the Art

In the field of sexual stimulation devices, control systems arerudimentary, and primarily limited to pre-programmed, selectablestimulation routines. Where customization is possible, it is availableonly through manual programming of the device. Control systems requiringmanipulation of physical or touch-screen controls can be cumbersome ordistracting.

What is needed is thought-based control of sexual stimulation devices.

SUMMARY

Accordingly, the inventor has conceived, and reduced to practice, asystem and method for though-based control of sexual stimulation devicesusing electroencephalography. The system and method involve a trainingphase and an operation phase. In an embodiment, the training phasecomprises placing electrodes on the head of a person, measuringelectrical signals produced by the person's brain via the electrodeswhile the user engages in one or more thought-based training tasks,associating patterns of electrical activity in the person's brain whileperforming the tasks with controls of the sexual stimulation device. Inan embodiment, the operation phase comprises generating control signalsfor the sexual stimulation device based on the associations when thepatterns of electrical activity are detected by the electrodes. In someembodiments, machine learning algorithms are used to detect the patternsof electrical activity and make the associations. In some embodiments,data from other biometric sensors is included in the associations.

According to a preferred embodiment, a system for thought-based controlof sexual stimulation devices is disclosed, comprising: a computingdevice comprising a memory, a processor, and a non-volatile data storagedevice; a database stored on the non-volatile data storage devicecomprising electroencephalograph (EEG) training tasks, each trainingtask comprising a stimulus, an objective related to a control of asexual stimulation device, and instructions for the user to attempt toachieve the objective using some mental image or thought; anelectroencephalograph (EEG) headset comprising a plurality of electrodesconfigured to detect electrical activity of a human brain when the EEGheadset is worn on the head of a person and transmit EEG signal dataassociated with the EEG signal data; an EEG training and controlapplication comprising a first plurality of programming instructionsstored in the memory which, when operating on the processor, causes thecomputing device to: perform EEG control training by: retrieving an EEGtraining task from the database; presenting the stimulus of the EEGtraining task and the objective of the EEG training task to a personwearing the EEG headset; instructing the person to attempt to achievethe objective using some mental image or thought according to theinstructions of the EEG training task; receiving EEG signal data fromeach electrode of the EEG headset while the person is performing the EEGtraining task; identifying a pattern of EEG activity from the EEG signaldata; and associating the identified EEG pattern with the objective ofthe task to create an EEG pattern/objective pair; and generatethought-based control signals for a sexual stimulation device by:receiving the EEG signal data from each electrode of the EEG headsetwhile the person is not performing the EEG training task; identifyingthe pattern of EEG activity from the EEG signal data; retrieving the EEGpattern/objective pair; and generating a control signal for the sexualstimulation device based on the objective of the EEG pattern/objectivepair.

According to another preferred embodiment, a method for thought-basedcontrol of sexual stimulation devices is disclosed, comprising the stepsof: storing a database stored on a non-volatile data storage device of acomputing device comprising a memory, a processor, and the non-volatiledata storage device, the database comprising electroencephalograph (EEG)training tasks, each training task comprising a stimulus, an objectiverelated to a control of a sexual stimulation device, and instructionsfor the user to attempt to achieve the objective using some mental imageor thought; using an electroencephalograph (EEG) headset comprising aplurality of electrodes to detect electrical activity of a human brainwhen the EEG headset is worn on the head of a person and to transmit EEGsignal data associated with the EEG signal data to an EEG training andcontrol application operating on the computing device; using the EEGtraining and control application operating on the computing device to:perform EEG control training by: retrieving an EEG training task fromthe database; presenting the stimulus of the EEG training task and theobjective of the EEG training task to a person wearing the EEG headset;instructing the person to attempt to achieve the objective using somemental image or thought according to the instructions of the EEGtraining task; receiving EEG signal data from each electrode of the EEGheadset while the person is performing the EEG training task;identifying a pattern of EEG activity from the EEG signal data; andassociating the identified EEG pattern with the objective of the task tocreate an EEG pattern/objective pair; and generate thought-based controlsignals for a sexual stimulation device by: receiving the EEG signaldata from each electrode of the EEG headset while the person is notperforming the EEG training task; identifying the pattern of EEGactivity from the EEG signal data; retrieving the EEG pattern/objectivepair; and generating a control signal for the sexual stimulation devicebased on the objective of the EEG pattern/objective pair.

According to an aspect of an embodiment, the EEG signal data is passedthrough to a trained machine learning algorithm and the trained machinelearning algorithm performs the tasks of: identifying the pattern of EEGactivity from the EEG signal data; and associating the identified EEGpattern with the objective of the task to create the EEGpattern/objective pair.

According to an aspect of an embodiment, the database further compriseslabeled data comprising a plurality of additional EEG pattern/objectivepairs from other persons who have engaged in the EEG training tasks, andthe machine learning algorithm is a supervised machine learningalgorithm that has been trained on the labeled data.

According to an aspect of an embodiment, the EEG control trainingprocess is repeated a plurality of times, generating a plurality of EEGpattern/objective pairs for the person wearing the EEG headset; and thesupervised machine learning is retrained for the person wearing the EEGheadset using the plurality of EEG pattern/objective pairs generated forthat person.

According to an aspect of an embodiment, the database further comprisesunlabeled data comprising EEG signal data and task objectives from otherpersons who have engaged in the EEG training tasks and the machinelearning algorithm is an unsupervised machine learning algorithm thathas been trained on the unlabeled data.

According to an aspect of an embodiment, the EEG control trainingprocess is repeated a plurality of times, generating a plurality of EEGsignal data and objectives for the person wearing the EEG headset; andthe unsupervised machine learning is retrained for the person wearingthe EEG headset using the plurality of EEG signal data and objectivesgenerated for that person.

BRIEF DESCRIPTION OF THE DRAWING FIGURES

The accompanying drawings illustrate several aspects and, together withthe description, serve to explain the principles of the inventionaccording to the aspects. It will be appreciated by one skilled in theart that the particular arrangements illustrated in the drawings aremerely exemplary, and are not to be considered as limiting of the scopeof the invention or the claims herein in any way.

FIG. 1 shows the internal workings of an exemplary sexual stimulationdevice.

FIG. 2 shows additional components of the internal workings of anexemplary sexual stimulation device.

FIG. 3 shows the external structure of an exemplary sexual stimulationdevice.

FIG. 4 shows exemplary variations of the sleeve and gripper aspects ofan exemplary sexual stimulation device.

FIG. 5 shows the internal workings of an exemplary sexual stimulationdevice.

FIG. 6 shows additional exemplary aspects of an exemplary sexualstimulation device.

FIG. 7 is a block diagram of an exemplary synchronized video controlsystem for sexual stimulation devices.

FIG. 8 is a block diagram of the video analysis engine aspect of anexemplary synchronized video control system for sexual stimulationdevices.

FIG. 9 is a block diagram of the control interface aspect of anexemplary synchronized video control system for sexual stimulationdevices.

FIG. 10 is a block diagram of the device controller aspect of anexemplary synchronized video control system for sexual stimulationdevices.

FIG. 11 is a flow diagram showing a method for an exemplary synchronizedvideo control system for sexual stimulation devices.

FIG. 12 is a flow diagram showing a method for using annotated videodata to control a sexual stimulation device.

FIG. 13 is a flow diagram showing a method for manual annotation ofvideos containing depictions of sexual activity.

FIG. 14 is a block diagram showing an exemplary system architecture forautomated annotation of videos containing depictions of sexual activity.

FIG. 15 (PRIOR ART) is a diagram describing the use of the local binarypattern (LBP) algorithm to extract the textural structure of an imagefor use in object detection.

FIG. 16 (PRIOR ART) is a diagram describing the use of a convolutionalneural network (CNN) to identify objects in an image by segmenting theobjects from the background of the image.

FIG. 17 is a diagram showing exemplary video annotation data collectionand processing to develop models of sexual activity sequences.

FIG. 18 is a flow diagram showing a method for an exemplary synchronizedvideo control system for sexual stimulation devices.

FIG. 19 is an exemplary system architecture diagram for a system forautomated control of sexual stimulation devices.

FIG. 20 is an exemplary algorithm for an automated set-up wizard for asystem for automated control of sexual stimulation devices.

FIG. 21 is an exemplary screenshot of a user interface for viewing,adjustment, and rating of automated control settings for a sexualstimulation device.

FIG. 22 is an exemplary system architecture diagram for a system forthought-based control of sexual stimulation devices.

FIG. 23 is an exemplary system architecture diagram for anelectroencephalograph training and control application aspect of asystem for thought-based control of sexual stimulation devices.

FIG. 24 is an exemplary algorithm for electroencephalograph data captureand machine learning algorithm training for thought-based control ofsexual stimulation devices.

FIG. 25 is an exemplary illustration of an electroencephalograph headsetfor use in thought-based control of sexual stimulation devices.

FIG. 26 is an exemplary illustration electroencephalograph sensorplacement and data patterns of an electroencephalograph headset for usein thought-based control of sexual stimulation devices.

FIG. 27 is a block diagram illustrating an exemplary hardwarearchitecture of a computing device.

FIG. 28 is a block diagram illustrating an exemplary logicalarchitecture for a client device.

FIG. 29 is a block diagram showing an exemplary architecturalarrangement of clients, servers, and external services.

FIG. 30 is another block diagram illustrating an exemplary hardwarearchitecture of a computing device.

DETAILED DESCRIPTION

The inventor has conceived, and reduced to practice, a system and methodfor automated generation of control signals for sexual stimulationdevices from usage history and other data. The system and method involveanalyzing historical usage and other data for a user for a device ordevices, processing the data through machine learning algorithms, andgenerating new or recombined patterns of stimulation based on theoutputs from the machine learning algorithms. The resulting automatedcontrol signals represent partially or fully customized stimulation fora given user which evolve over time as the user continues to use thedevice or devices. In some embodiments, the machine learning algorithmwill be trained on usage data from a large number of users of the samedevice or similar devices or the statistical analysis will be performedon such usage data.

This automated generation of control signals from historical usage andother data, and evolution of the control signals over time, acts as asort of “autopilot” for sexual stimulation devices such that a prioriprogramming or manual programming of the devices either not required atall or is minimal in nature. The device can simply be turned on andstimulation will be automatically customized to the user's preferenceswith little or no input on the user's part.

One or more different aspects may be described in the presentapplication. Further, for one or more of the aspects described herein,numerous alternative arrangements may be described; it should beappreciated that these are presented for illustrative purposes only andare not limiting of the aspects contained herein or the claims presentedherein in any way. One or more of the arrangements may be widelyapplicable to numerous aspects, as may be readily apparent from thedisclosure. In general, arrangements are described in sufficient detailto enable those skilled in the art to practice one or more of theaspects, and it should be appreciated that other arrangements may beutilized and that structural, logical, software, electrical and otherchanges may be made without departing from the scope of the particularaspects. Particular features of one or more of the aspects describedherein may be described with reference to one or more particular aspectsor figures that form a part of the present disclosure, and in which areshown, by way of illustration, specific arrangements of one or more ofthe aspects. It should be appreciated, however, that such features arenot limited to usage in the one or more particular aspects or figureswith reference to which they are described. The present disclosure isneither a literal description of all arrangements of one or more of theaspects nor a listing of features of one or more of the aspects thatmust be present in all arrangements.

Headings of sections provided in this patent application and the titleof this patent application are for convenience only, and are not to betaken as limiting the disclosure in any way.

Devices that are in communication with each other need not be incontinuous communication with each other, unless expressly specifiedotherwise. In addition, devices that are in communication with eachother may communicate directly or indirectly through one or morecommunication means or intermediaries, logical or physical.

A description of an aspect with several components in communication witheach other does not imply that all such components are required. To thecontrary, a variety of optional components may be described toillustrate a wide variety of possible aspects and in order to more fullyillustrate one or more aspects. Similarly, although process steps,method steps, algorithms or the like may be described in a sequentialorder, such processes, methods and algorithms may generally beconfigured to work in alternate orders, unless specifically stated tothe contrary. In other words, any sequence or order of steps that may bedescribed in this patent application does not, in and of itself,indicate a requirement that the steps be performed in that order. Thesteps of described processes may be performed in any order practical.Further, some steps may be performed simultaneously despite beingdescribed or implied as occurring non-simultaneously (e.g., because onestep is described after the other step). Moreover, the illustration of aprocess by its depiction in a drawing does not imply that theillustrated process is exclusive of other variations and modificationsthereto, does not imply that the illustrated process or any of its stepsare necessary to one or more of the aspects, and does not imply that theillustrated process is preferred. Also, steps are generally describedonce per aspect, but this does not mean they must occur once, or thatthey may only occur once each time a process, method, or algorithm iscarried out or executed. Some steps may be omitted in some aspects orsome occurrences, or some steps may be executed more than once in agiven aspect or occurrence.

When a single device or article is described herein, it will be readilyapparent that more than one device or article may be used in place of asingle device or article. Similarly, where more than one device orarticle is described herein, it will be readily apparent that a singledevice or article may be used in place of the more than one device orarticle.

The functionality or the features of a device may be alternativelyembodied by one or more other devices that are not explicitly describedas having such functionality or features. Thus, other aspects need notinclude the device itself.

Techniques and mechanisms described or referenced herein will sometimesbe described in singular form for clarity. However, it should beappreciated that particular aspects may include multiple iterations of atechnique or multiple instantiations of a mechanism unless notedotherwise. Process descriptions or blocks in figures should beunderstood as representing modules, segments, or portions of code whichinclude one or more executable instructions for implementing specificlogical functions or steps in the process. Alternate implementations areincluded within the scope of various aspects in which, for example,functions may be executed out of order from that shown or discussed,including substantially concurrently or in reverse order, depending onthe functionality involved, as would be understood by those havingordinary skill in the art.

FIG. 1 shows the internal workings of an exemplary sexual stimulationdevice 100. The compatible device is a small handheld unit powered by alow voltage, external direct current (DC) power source. Inside thedevice is a metal framework 101 to which the mechanical parts of thedevice are attached. Attached to the metal framework 101 is a small DCmotor 102 with a motor shaft 103, which drives the stimulationmechanism. A screw shaft 104 is affixed to the motor shaft 103 of the DCmotor 102, such that the screw shaft 104 rotates as the motor shaft 103of the DC motor 102 rotates. The polarity of voltage to the DC motor 102may be reversed so that the motor shaft 103 of the DC motor 102 rotatesboth clockwise and counter-clockwise. A flex coupling 105 between themotor shaft 103 of the DC motor 102 and screw shaft 104 compensates forany misalignment between the two during operation. A screw collar 106 isplaced around the screw shaft 104 and attached to a bracket 107, whichis held in a particular orientation by guide rods 108, such that thescrew collar 106 and bracket 107 travel in a linear motion as the screwshaft 104 is turned. Affixed to the bracket 107 is a gripper 109, whichtravels in a linear motion along with the bracket 107. A hole 110 in themetal framework 101, allows for the insertion of a flexible sleeve asshown in FIG. 2 .

FIG. 2 shows additional components of the internal workings of anexemplary sexual stimulation device 200. A flexible sleeve 201 made ofeither thermoplastic elastomer (TPE) or thermoplastic rubber (TPR) isinserted through a large hole 109 in the metal framework 101 and throughgripper 108. Sleeve 201 is prevented from accidentally slipping intodevice 200 by a ridge 202 at the open end of sleeve 201, and is held inthe proper position by ridges 203 at both ends of gripper 108. Duringoperation, gripper 108 slides in a reciprocal linear motion 201providing pressure and motion against the penis inside the sleeve 201 ina manner similar to sexual intercourse or manual masturbation. Dependingon the configuration, gripper 108 may either grip sleeve 201 and movesleeve 201 along the penis, or it may slide along the outside of sleeve201, not moving the sleeve relative to the penis. Also depending onconfiguration, gripper 108 may be made of rigid, semi-rigid, orcompliant materials, and other shapes might be used (e.g., partial tube,ring, half-ring, multiple rings, loops of wire) and may contain rollersor bearings to increase stimulation and reduce friction against theflexible sleeve 201.

FIG. 3 shows the external structure 300 of an exemplary sexualstimulation device. The housing 301 of the device is made of plastic,and is attached to the metal framework in such a way as to provideadditional support and structure to the device. User controls 302 in theform of buttons and switches and their associated electronics are builtinto the housing. The housing has an opening at one end corresponding tothe opening 109 in the metal framework 101, into which the flexiblesleeve 201 is inserted. The penis is inserted into the sleeve 201 at theend of the device, and is stimulated by the reciprocal linear motion ofthe gripper 108 inside the device. The user controls the speed, pattern,and location of stimulation using the controls 302 on the outside of thehousing 301.

FIG. 4 shows exemplary variations 400 of the sleeve 201 and gripper 108aspects of an exemplary sexual stimulation device. As noted above,different configurations of the sleeve 201 and gripper 108 are possibleto allow optimal fit and sensation for penises of different lengths andgirths, and to allow the user a choice of pressure, gripper location,and sensation. Sleeve variant one 401 has a thin top wall 402 with a lowpoint of attachment 403 to the gripper 108. Sleeve variant two 404 has athin top wall 405 with a middle point of attachment 406 to the gripper108. Sleeve variant three 407 has a uniform wall thickness 408 with amiddle point of attachment 409 to the gripper 108. Sleeve variant four410 has a bellows top 411, a thin wall 412, and a middle point ofattachment 413. Sleeve variant five 414 has an extended bellows 415 andno attachment to the gripper 108 other than a stopper at the end 416,allowing the gripper 108 to slide along the outside of the sleeve 414.Sleeve variant six 417 has a uniform wall thickness 418 and noattachment to the gripper 108 other than a stopper at the end 419,allowing the gripper 108 to slide along the outside of the sleeve 417.Sleeve variant seven 420 has a full bellows design 421 and no attachmentto the gripper 108 other than a stopper at the end 422, allowing thegripper 108 to slide along the outside of the sleeve 420. Sleeve varianteight 423 has a full bellows design with large grooves 424 into whichfits a gripper made of wire loops with beads attached 425.

FIG. 5 shows the internal workings of an exemplary sexual stimulationdevice 500. The compatible device is a small handheld unit powered by alow voltage, external direct current (DC) power source. Inside thedevice is a metal framework 501 to which the mechanical parts of thedevice are attached. Attached to the metal framework 501 is a small DCmotor 502 with a motor shaft 503, which drives the stimulationmechanism. A screw shaft 504 is affixed directly to the motor shaft 503of the DC motor 502, such that the screw shaft 504 rotates as the motorshaft 503 of the DC motor 502 rotates. The polarity of voltage to the DCmotor 502 may be reversed so that the motor shaft 503 of the DC motor502 rotates both clockwise and counter-clockwise. In this embodiment,the flex coupling 105 has been eliminated, allowing the device to beconstructed in a more compact form, approximately 2 cm shorter inoverall length. A screw collar 505 is placed around the screw shaft 504and attached to a bracket 506, which is held in a particular orientationby guide rods 507, such that the screw collar 505 and bracket 506 travelin a linear motion as the screw shaft 504 is turned. Affixed to thebracket 506 is a gripper 508, which travels in a linear motion alongwith the bracket 506. A hole 509 in the metal framework 501, allows forthe insertion of a flexible sleeve 201 as previously shown in FIG. 2 .FIG. 6 shows additional exemplary variations 600 of the sleeve aspect ofan exemplary sexual stimulation device as set forth in another preferredembodiment. In this embodiment, the opening in the sleeve may be otherthan circular. For example, the opening may be elliptical in shape 601or triangular in shape 602.

FIG. 6 shows additional exemplary variations of the aspects of anexemplary sexual stimulation device.

FIG. 7 is a block diagram of an exemplary synchronized video controlsystem for sexual stimulation devices 700. In a this embodiment, a videoanalysis engine 701 inputs a video of sexual activity, parses the videointo at least the components of movement corresponding to the sexualactivity shown in the video, and outputs signals containing the parsedvideo information to a device controller 702. A control interface 703allows the user to enter a profile containing parameters for sexualstimulation device operation or the user's biometric information, storesthe user's profile information, and outputs the user's profileinformation to the device controller 702. The device controller 702adjusts the signals from the video analysis engine 701 based on theprofile information from the control interface 703 and outputs theadjusted signals to a stimulation device 704 such that they aresynchronized with the activity shown in the video. In an aspect of anembodiment, the parsed video information from the video analysis engine701 is stored in a data storage device 705 for later retrieval and use.

FIG. 8 is a block diagram 800 the video analysis engine 701 aspect of anexemplary synchronized video control system for sexual stimulationdevices. A video parser 801 receives video input 802, sends the video'smetadata to a metadata processor 803, which checks to see if themetadata for that video already exists in the data storage device 705.If the metadata already exists, it is read from the data storage device705 and sent out the control interface 703. If the metadata does notexist, it is formatted, written to the data storage device 705, and sentout to the control interface 703. Simultaneously, the video parser 801sends the video content to the motion translation processor 804, whichchecks to see if the control signal data for that video already existsin the data storage device 705. If the control signal data alreadyexists, it is read from the data storage device 705 and sent out thedevice controller 702. If the control signals do not exist, the motiontranslation processor 804 uses video processing algorithms and machinelearning algorithms to detect sexual activity and to translate themotions in the video to control signals related to movement, pressure,and rhythm, and makes adjustments to the control signals in response todata from the control interface 805. The controls signals are thenwritten to the data storage device 705 and sent out to the devicecontroller 702. In an aspect of an embodiment, the actual video contentmay also be stored in the data storage device 705.

FIG. 9 is a block diagram 900 of the control interface 703 aspect of anexemplary synchronized video control system for sexual stimulationdevices. The user can enter device parameter settings 901 to adjustoperation of a compatible device. The user can further enter biometricdata manually, or it may be obtained automatically by the biometric datainterface 902 from biometric sensor receiver 1004 disclosed in FIG. 10 .The parameters and biometric data are sent to a profile generator 903,which creates a profile for the user based on the various inputs. Theprofile information is saved to the storage device 705, and is sent tothe device controller 702. The control interface may contain a manualvideo tagging interface 904, which allows the user to adjust thesensations received while viewing those videos.

FIG. 10 is a block diagram 1000 of the device controller 702 aspect ofan exemplary synchronized video control system for sexual stimulationdevices. Control signals for the video being watched are received fromthe motion translation processor 804 into the video synchronizer 1001,which adjusts the timing of the signals to correspond with the videobeing watched. Parameters and biometric data are received into theprofile interface 1002 from the profile generator 903. A control signalgenerator 1003 receives the outputs from both the video synchronizer1001 and profile interface 1002, and adjusts the synchronized controlsignals based on the parameters and biometric data, and sends out theadjusted control signal to the stimulation device 704. The devicecontroller may also contain a biometric sensor receiver 1004 that couldallow the capture of biometric data from wireless devices such asfitness trackers that monitor heart rate, blood pressure and breathingmonitors, and even sensors in the stimulation device itself. The datacaptured through the biometric sensor receiver could be used for realtime feedback to the control signal generator 1003 and for use inimproving user experiences by enhancing the user's profile or improvingthe accuracy of video selection.

FIG. 11 is a flow diagram showing a method 1100 for an exemplarysynchronized video control system for sexual stimulation devices.According to this method, video of sexual activity would be input into acomputer 1101. The computer, using machine learning algorithms, wouldparse the video into at least one component corresponding to the sexualactivity shown in the video 1102. The parsed video information could bestored for later retrieval 1103 and any video metadata could also bestored for later retrieval 1104. Signals containing the parsed videoinformation to a device controller would be output to a devicecontroller 1105. Separately, the user would be allowed to enter aprofile in a control interface containing at least parameters foradjusting compatible device operation 1106, and biometric data 1107,which would be stored 1108, and output to the device controller 1109.The signals from the parsed video would be adjusted based on the user'sprofile information 1110 and output to a compatible device, synchronizedwith the activity shown in the video, such that the compatible devicewould emulate the sexual activity shown in the video 1111.

FIG. 12 is a flow diagram showing a method for using annotated videodata to control a sexual stimulation device. In a first step, videoscontaining depictions of sexual activity are annotated (or tagged) withdata regarding one or more movements shown in the videos 1201. Theannotations are associated with playback times in the video, either asmetadata incorporated into the video file or as separate files. Theannotations (or tags) may be performed manually by a person watching thevideo or automatically by the video analysis engine 701.

The annotations may be used directly to generate device control signals1205, such as real-time use wherein the device control signals aregenerated 1205 immediately or very soon after the annotations arecreated, or delayed use by storing the annotations for later use 1202and generating device control signals 1205 from the stored annotations.In this use, the annotations will typically be used to generate controlsignals for a particular video for which the annotations were made. Asingle such annotation may be used or some combination of annotationsfor the same video (e.g., averaging of multiple annotations).

Alternatively, the annotations may be processed through machine learningalgorithms to create models of movement patterns and sequences commonlyassociated with certain videos, or certain sexual activities, persons,etc. In this use, annotations from a plurality of different videos willtypically be used. The annotations are processed through a first set ofmachine learning algorithms to detect and analyze movement patternstypical of certain sexual activities 1203. This first set of machinelearning algorithms may use techniques such as clustering to grouptogether similar types of movement patterns. The movement pattern dataare then processed through a second set of machine learning algorithmsto determine sequencing information 1204 such as how long a pattern istypically held and the probabilities of changing to different patternsafter the current pattern. The sequencing information is used to createpredictive models of typical or expected sequences of movement patterns,which mimic frequently-seen depictions of sexual activity in theannotated data. The data from these models may then be used to generatedevice control signals 1205 representing movement patterns and sequencesin common sexual activities.

FIG. 13 is a flow diagram showing a method for manual annotation ofvideos containing depictions of sexual activity. In a first step, avideo is played which contains depictions of sexual activity 1301.During playback, a human viewer moves a controller to indicate therelative motion of a movement of sexual activity located on the screen.The controller may be any device that allows the viewer to input amotion associated with a movement of sexual activity in the video beingviewed by the viewer 1302. Ideally, the controller will allow the viewerto simply imitate the motion by mimicking the motion(s) seen in thevideo (e.g., moving the viewer's hand back and forth) rather thanprogramming in the motion(s) (e.g., by entering a number associated withthe motion). The controller may be virtual (e.g., an on-screen sliderbar, an on-screen virtual joystick, gestures made in front of agesture-recognition camera), or the controller may be a physical device(e.g., a physical slider, joystick, wand, mobile phone with anaccelerometer, etc.). The controller may allow for linear motions,two-dimensional motions, or three-dimensional motions, and may alsoallow for rotation or tilting. As the human viewer moves the controllerin synchronicity with the movements depicted in the video, annotationdata are created that are associated with video playback times 1303. Asa simple example, a reciprocal motion depicted in the video may beannotated as tuples, with a series of time stamps representing the videoplayback time, each associated with a value indicating the relativelocation of the linear motion in the video at that time. The annotationsmay be incorporated into the video file as metadata or stored asseparate data files. Where the annotations will be used to generatedevice control data for a particular video, the annotation willtypically be associated with the video in some manner. However, wherethe annotations are to be used as input to machine learning algorithmsfor generation of models of sexual activity, the annotations may bedisassociated with the video from which they are derived. Theannotations may then be used to generate control signals 1305, or may beprocessed through machine learning algorithms to detect patterns ofmovement and create model sequences of such patterns mimicking themovements of sexual activity associated with certain concepts (e.g.,frequently-seen movements represented in a certain type of video, orcertain sexual activities, or associated with certain actors andactresses, etc.) 1304.

FIG. 14 is a block diagram showing an exemplary system architecture forautomated annotation of videos containing depictions of sexual activity.This exemplary system architecture provides more detail regarding theoperation of the video analysis engine 701. In some embodiments, thisexemplary system architecture, or a similar one, may be incorporatedinto the video analysis engine 701 as a component, or as a component ofthe video parser 801, the metadata processor 803, or the motiontranslation processor 804. In some embodiments, this system architecturemay be distributed among, or substitute for, one or more components ofthe video analysis engine 701. In some embodiments, this systemarchitecture or it components may exist separately from, but remainaccessible to, the video analysis engine 701.

In this exemplary embodiment, a clip parser 1401 parses (i.e., breaksbreaks or segments) a video into smaller clips to reduce the scale ofthe video processing by the machine learning algorithms (i.e., reducesthe video to more easily manageable smaller clips of a larger video).Depending on the size of the video, available processing power, and themachine learning algorithm to be used, the clip parser 1401 may reducethe video to any size ranging from the entire video to frame-by-frameclips of the video. Where a video is annotated with known activities(e.g., where the video or segments of the video have been annotated withan indication of the type of activity that is contained therein), theclip parser 1401 may parse the video into clips corresponding to thelength of the known activity, as indicated by the annotations. In suchcases, the clip parser 1401 forwards the clips of known activitydirectly to an action detector 1402. Where the video contains depictionsof unknown activities, the clip parser will parse the video into uniformsizes (e.g., frame-by-frame, or a certain number of frames representingseveral seconds or minutes of video), and send the video to an actionclassifier 1403, which classifies the activities in the video beforesending them an known activities to the action detector 1402.

The action classifier 1403 comprises one or more machine learningalgorithms that have been trained to classify human actions.Classification of human action is a simpler activity than human actiondetection. Human action classification involves identification of humanobjects in the video and some classification of the activity beingdemonstrated by the human objects (e.g., standing, walking, running,jumping, etc.). Classification does not require a determination of whenthe action starts, where in the frame the action occurs, or the relativemotion of the action; it simply requires that an object in the video berecognized as a person and that the activity of that person beidentified.

The action detector 1402 received videos of known sexual activity (i.e.,those that have already been classified either manually or using machinelearning algorithms), and detects when the action starts, where in theframe the action occurs, or the relative motion of the action. Becausethe activity in the video is already known, machine learning algorithmsmay be employed which have been specially-trained for the type ofactivity depicted in the video. Action detection involves firstsegmenting the video into objects and backgrounds, identifying humanobjects in each frame of video, and tracking the movement of those humanobjects across video frames.

Both action classification and action detection rely on color-basedprocessing of pixels in each frame of the video. Most videos currentlyavailable, whether or not depicting sexual activity, are two-dimensional(2D) videos containing color information only (e.g., the RGB colormodel), from which depth information must be inferred. The additional ofdepth sensors allows the addition of depth information to the video data(e.g., RGBD color/depth model), which improves human pose estimation butrequires specialized sensors that must be used at the time of filming.Due to the processing-intensive nature of analyzing videos using machinelearning algorithms, some simplification techniques may be used toreduce the computing power required and/or speed up the processing time.For example, facial recognition algorithms have become widely used,fairly accurate, and can be implemented on computing devices with modestprocessing power. Thus, for videos where fellatio is known to be theprimary sexual activity, facial recognition algorithms may be used asthe machine learning component to track the relative position andorientation of the face in the video to indicate the movement componentof sexual activity. This greatly reduces the amount of computing powerrequired relative to videos containing unknown sexual activity and/orwhere whole body human activity must be classified and detected. Asthere is a limited range of possible sexual activity, and certain sexualactivities are more common than others, specially-trained machinelearning algorithms can be employed for given types of sexual activityto improve action classification and action detection times andaccuracy.

For both action classification and action detection, a variety ofmachine learning algorithms may be used. For example, as noted above, aconvolutional neural network (CNN) may be applied to performsegmentation of each video frame. Other machine learning algorithms orcombinations of machine learning algorithms may be employed. Forexample, a CNN may be employed to extract the features in the video,followed by a long short-term memory (LSTM) algorithm to evaluate thetemporal relationships between features. In another example, athree-dimensional CNN (3D CNN) may be employed which can directly createhierarchical representations of spatial and temporal relationships, thusobviating the need to processing through an LSTM. In another example, atwo-stream CNN may be used, wherein the first stream of input into theCNN is a set of temporal relationships that are established by apre-determined set of features, and the second stream is frames from thevideo. Action classification and/or action detection can be performed byaveraging the predictions of the CNN, or by using the output of the CNNfor each frame of the video as input to a 3D CNN. Many other variationsare possible, and while CNNs are particularly suitable for videoprocessing, other types of machine learning algorithms may be employed.

The clip annotator 1404 associates each video clip with action detectiondata synchronized with the playback times (or frames) of the video clip,and the clip re-integrator 1405 combines the clips back into theoriginal video received by the clip parser 1401. The annotated video, orjust the annotations data from the video, may then be used to generatedevice control data or may be further processed to extract models oftypical sexual activity prior to generating device control data.

FIG. 15 (PRIOR ART) is a diagram describing the use of the local binarypattern (LBP) algorithm to extract the textural structure of an imagefor use in object detection. There are a wide variety of algorithms forextracting data from images and/or video (which is a series of images)for object recognition within the image. The local binary pattern (LBP)algorithm is one of the simplest and easiest to understand, and istherefore used here to demonstrate in general terms how image data isprocessed to extract certain information. All digital images arecomposed of pixels, each of which represents the smallest area ofviewable information in the image (i.e., each pixel is a “dot” in theimage). Each pixel contains information about the color that the dotrepresents, and the color of the pixel may be either black and white,grayscale, or colored. The representation of the color may be in anynumber of standard formats (also called color models), with thehexadecimal (HEX), red, green, blue (RBG), and cyan, magenta, yellow,key/black (CMYK) being three of the most common. In this simplifiedexample, the original image 1501 is in 256-bit grayscale, meaning thateach pixel in the original image 1501 has a grayscale value of 0-255.The LBP algorithm is applied to each pixel in the original image 1501 byselecting a pixel and comparing the value of that pixel to the value ofeach surrounding pixel, as shown in the first table of values 1502, inwhich the selected pixel from the original image 1501 has a value of 90,and the values of the surrounding pixels from top left and goingclockwise are 30, 50, 70, 120, 220, 180, 80, and 20. In a next step ofthe LBP algorithm, for each of the pixels in the first table 1502 isassigned a binary (zero or one) value in a second table 1503, wherein azero is assigned if the value of the pixel is less than the value of theselected (i.e., center) pixel, and a one is assigned if the value of thepixel is equal to or greater than the value of the selected (i.e.,center) pixel. The resulting values are shown in the second table 1503,wherein the pixels with values of 90, 120, 220, and 180 have beenassigned a binary value of one, and all of the other pixels have beenassigned a value of zero. The values of each of the pixels in the secondtable 1503 surrounding the selected (i.e., center) pixel areconcatenated together in a clockwise manner starting from the top left,resulting in this case in the binary number 00011100. This binary numberis then converted back to a decimal number, in this case 28, and thisdecimal number is substituted in for the value of the selected pixel inthe original image 1501, representing a 256-bit grayscale value for thelocal area in which the selected pixel resides. This process is repeatedfor all pixels in the original image 1501, resulting in a texturizedimage 1504 wherein each pixel represents the “texture” of thesurrounding pixels from the original image 1501. Many differentprocessing methods can be used on the texturized image to identifyfeatures and objects in the texturized image, such division of the imageinto blocks and extracting histograms of each block, and running thehistograms through machine learning algorithms that have been trained toidentify features from similar histograms from similar images.

FIG. 16 (PRIOR ART) is a diagram describing the use of a convolutionalneural network (CNN) to identify objects in an image by segmenting theobjects from the background of the image. Artificial neural networks arecomputing systems that mimic the function of the biological neuralnetworks that constitute human and animal brains. Artificial neuralnetworks comprise a series of “nodes” which loosely model the neurons inthe brain. Each node can pass on a signal to other nodes. The output ofeach node is some non-linear function of the sum of its inputs, and theprobability of a signal being passed to another node depends on theweight assigned to the “edge” between the nodes, which is the connectionbetween the nodes. An artificial neural network finds the correctmathematical relationship between an input and an output by calculatinga probability of obtaining the output from the input at each “layer” ofmathematical calculations.

Convolutional neural networks are a type of artificial neural networkcommonly used to analyze imagery that use a mathematical operationcalled convolution (also called a dot product or cross-correlation)instead of general matrix multiplication as in other types of artificialneural networks. Convolutional neural networks are fully connected,meaning that each node in one layer is connected to every node in thenext layer. Each layer of the CNN convolves the input from the previouslayer. Each convolutional node processes data only for its receptivefield, which is typically a small sub-area of the image (e.g., a 5×5square of pixels). There may be pooling layers in a CNN which reduce thedimensionality of the data by combining the outputs of node clusters inone layer into a single node in the next layer. Each node in a CNNcomputes an output value by applying a specific function to the inputvalues coming from the receptive field in the previous layer. Thefunction that is applied to the input values is determined by a vectorof weights and a bias. The CNN “learns” by making iterative adjustmentsto these biases and weights.

In this application of CNNs, an input image 1601 is processed through aCNN in which there are two stages, a convolution stage 1602 and ade-convolution stage 1603, ultimately resulting in an output image 1604in which objects in the image are segmented (i.e., identified asseparate from) the background of the image. In the convolution stage1602, the image is processed through multiple convolution layers toextract features from the image, and then through a pooling layer toreduce the dimensionality of the data (i.e., aggregation of pixels) forthe next round of convolutions. After several rounds of convolution andpooling, the features have been extracted and the data have been reducedto a manageable size. The data are then passed to the de-convolutionstage 1603, in which a prediction is made as to whether each pixel orgroup of pixels represents an object, and passed through several layersof de-convolution before a new prediction is made at a larger level ofde-aggregation of the pixels. This process repeats until an output image1604 is obtained of a similar size as the input image 1601, wherein eachpixel of the output image 1604 is labeled with an indication as towhether it represents an object or background.

FIG. 17 is a diagram showing exemplary video annotation data collectionand processing to develop models of sexual activity sequences. In afirst step, annotation data from videos depicting sexual activity isgathered. The diagram at 1710 shows an exemplary graph created fromannotation data from a single video depicting sexual activity. The graphof the annotation data shows the relative position of an object in asingle video over time (i.e., movement of the object over time in thatvideo). A number of patterns of movement 1711-1715 can be seen in thegraph. When used in conjunction with a single video, the annotation datacan be converted directly into device control data for a sexualstimulation device, and the device can be used in synchronization withthe video just from the annotation data for that video. However, ifmodels of sexual activity are to be created for use with the sexualstimulation device (e.g., to mimic “typical” sexual activities butwithout reference to a particular video), additional processing isrequired to develop models from the annotated data.

To process annotation data to develop models, patterns of movement willideally be extracted from a larger number of videos. When a machinelearning algorithm is fed the annotation data from many such videos,these patterns can be identified across the various videos, and thefrequency of these patterns across all videos can be extracted, as shownin the bar chart at 1720. In this bar chart 1720, one hundred totalhours of video time was processed through the machine learningalgorithm, and the number of hours each pattern of movement 1711-1715was displayed is shown. For example, Pattern 4 was displayed in a totalof 40 hours out of the 100 total hours of video. Machine learningalgorithms suitable for this identification of patterns across videosare clustering-type algorithms such as K-means clustering (also known asLloyd's algorithm), in which movement patterns in the annotation dataare clustered into groups containing similar movement patterns. From theclusters, certain types of movement patterns can be identified. Forexample, in the case of a video depicting fellatio, clusters of movementwill show shallow motions around the tip of the penis (e.g., Pattern 41714), deep motions around the base of the penis (e.g., Pattern 1),movements along the full length of the penis (e.g., Pattern 3), etc.Such clusters may be visually mapped in 2D or 3D to confirm theconsistency and accuracy of the clustering.

Finally, other types of machine learning algorithms may be employed tocreate models of sexual activity shown in the processed annotation data.In one method, reinforcement learning may be employed to identify thefrequency counts of certain patterns of movement, create “states”representing these patterns, and probabilities of transferring from anygiven state to any other state. An example of such a state diagram isshown at 1730, wherein each state represents one of the patterns ofmovement 1711-1715, and the lines and percentages indicate theprobability of transitioning to a different state. In the diagram at1730, Pattern 5 1715 is shown as the current state, and probabilities ofall possible transitions to and from the current state are shown. Inpractice, this state diagram 1730 would be expanded to include theprobabilities to and from each state to every other state, but thisdiagram is simplified to show only transitions to and from the currentstate. From these state transition probabilities, sequences of movementpatterns 1711-1715 may be constructed representing models of the“typical” activities shown in the video. If annotation data areprocessed for selected types of videos (e.g., videos containing certaintypes of sexual activity, certain actors or actresses, or videos from acertain film studio or director, etc.), the models will berepresentative of that selected type of video. Alternatively, a widevariety of deep learning algorithms may be used for this processincluding, but not limited to, dense neural networks, convolutionalneural networks, generative adversarial networks, and recurrent neuralnetworks. Each of these types of machine learning algorithms may beemployed to identify sequences of the patterns of movement identified inthe clustering at the previous stage.

FIG. 18 is a flow diagram showing a method for developing models ofsexual activity sequences from selected videos. In a first step,annotation data are received for a plurality of videos of a particulartype (e.g., videos containing certain types of sexual activity, certainactors or actresses, or videos from a certain film studio or director,etc.) 1801. Next, the annotation data are processed machine learningalgorithms to detect and classify patterns of movement 1802. Then, thedetected patterns of movement are further processed through machinelearning algorithms to identify sequences of patterns of movement thatare common for that selected type of video 1803, which are then turnedinto models representative of the types of sexual activity depicted.Optionally, the patterns and sequences of movement may be classifiedbased on metadata associated with the video or based on human input1804. For example, a particular sequence may be classified as a typicalrepresentation of fellatio by a particular adult film star from acertain decade. Lastly, after the models are created, device controlmodes or functions based on the models may be created 1805 and storedfor later use or programmed into the sexual stimulation device.

FIG. 19 is an exemplary system architecture diagram for a system forautomated control of sexual stimulation devices. In this embodiment, thesystem comprises a server 1910, a client application 1920, a stimulationdevice 1930, and data from other users and devices 1940.

The server may be a network-connected, cloud-based, or local server1910, and comprises a database 1911 for storage of usage data comprisinguser profiles, user/device feedback, and user/device settings, and amachine learning algorithm 1912 for analysis of the data stored in thedatabase 1911 for generation of automated control signals orinstructions. The machine learning algorithm 1912 is trained on the datato identify patterns within the usage data wherein certaincharacteristics of user profiles are correlated with satisfaction ordissatisfaction with certain aspects of stimulation profiles such astempo, location, intensity, pressure, and patterns. The usage data maycontain user profiles comprising personal information about the usersuch as age, sex, height, weight, and fitness level; sexual preferencessuch as straight, gay, bi-sexual, etc.; stimulation preferences such asstimulation tempo/speed, stimulation intensity, location of stimulation,patterns of stimulation; and feedback information such as user ratings,heartrate data from sensors, moisture data from sensors, etc. Aftertraining, when a user profile (or one or more characteristics from auser profile) is input into the machine learning algorithm 1912, themachine learning algorithm 1912 generates one or more stimulationprofiles (comprising one or more stimulation aspects such astempo/speed, stimulation intensity, location of stimulation, patterns ofstimulation) that correspond with satisfaction based on thecharacteristics of the user profile input and outputs control signals(or instructions for generating control signals) for stimulationprofiles that correspond with satisfaction based on the characteristicsof the user profile input. The machine learning algorithm 1912 mayperiodically or continuously be re-trained based on new data from theclient application 1920 (such as, but not limited to, feedback and otherchanges to the user's profile) and the data from other users and devices1940 being similarly stored and processed. It should be noted that,while a machine learning algorithm is used in embodiment, the system isnot necessarily limited to use of machine learning algorithms and otherprocesses for analysis of the data may be used, including but notlimited to modeling and statistical calculations.

The system of this embodiment further comprises a client application1920, which is a software application operating on a computing device,which may be of any type including but not limited to a desktopcomputer, tablet, mobile phone, or even a cloud-based server accessiblevia a web browser. The client application 1920 acts as an interfacebetween the stimulation device 1930 and the machine learning algorithm1912, relaying feedback from the device to the server 1910 and relayingcontrol signals (or translating instructions into control signals) tothe device controller 1932 of the stimulation device 1930. The clientapplication may comprise one or more applications such as the auto-pilotapplication 1921 and the wizard application 1922. Depending onconfiguration, the client application may further act as a userinterface for operation of, and/or changing settings of, the stimulationdevice 1930.

In this embodiment, the auto-pilot application 1921 automaticallycontrols the stimulation device 1930 for the user with little or noinput from the user. The auto-pilot application stores and retrievesuser-specific data for the user of the stimulation device 1930 from auser profile entered into the client application 1920, from sensors onthe device (e.g., tumescence sensors, heartrate sensors or heartratesignal receivers, pressure sensors, etc.), and from user interactionswith the client application 1920 via a user interface. The data gatheredabout the user may include such as, but not limited to, where the userprefers to be stimulated, what tempo or speed of stimulation the userprefers, what stimulation patterns the user prefers, and generalpreferences such as quick stimulation to orgasm, delayed orgasms,multiple edging before orgasm, etc.

The auto-pilot application 1921 provides the user-specific data to theserver 1910 and requests control signals (or instructions for controlsignals) for a stimulation profile that is customized to the user basedon the user data. The user-specific data is processed through thetrained machine learning algorithm 1912, which selects appropriatestimulation routines and provides control signals or instructions backto the client application for operation of the stimulation device 1930.In some embodiments the control signals or instructions may be sentdirectly from the machine learning algorithm 1912 directly to the devicecontroller 1932 of the stimulation device 1930. The client application1920 may be configured to periodically or continuously send updateduser-specific data to the server 1910 for processing by the machinelearning algorithm 1912 to generate modified or updated control signalsor instructions, thus changing and evolving the automated operation ofthe device based on changed or updated information from the devicesensors 1931, client application 1920, or updating/retraining of themachine learning algorithm 1912 based on this user's data and the datafrom other users and devices 1940 being similarly stored and processed.

In this embodiment, the set-up wizard application 1922 builds an initialpersonalized stimulation profile from a series of ratings by the user oftest stimulations. Completion of the set-up wizard application 1922process accelerates customization of a stimulation profile for the userby providing a base set of ratings of various aspects of stimulationwhich can then be processed through the trained machine learningalgorithm 1912 to automatically control the stimulation device 1930, asfurther shown in FIG. 20 . After completion of the set-up wizardapplication 1922, stimulation profiles for the user may continue toevolve from new user-specific data as described above. In someembodiments, the set-up wizard application 1922 and auto-pilotapplication 1921 operate independently from one another, while in otherin other embodiments the set-up wizard application 1922 is the firststep in the automated control process, followed by further automation bythe auto-pilot application 1921.

In some embodiments, the client application 1920 may exist as anapplication on a user's mobile phone, and may interface with thestimulation device 1930 via a local network (e.g., WiFi, Bluetooth,etc.). In other embodiments, the client application 1920 may exist as anapplication on the server 1920 accessible via a user account alsoresiding on the server. In other embodiments, certain components of theserver 1910 and client application 1920 may reside on tablet computer orother mobile device, or on the stimulation device 1930 itself (e.g., acopy of the trained machine learning algorithm could reside on asmartphone such that automated generation of control signals can beaccomplished without access to the server). In some embodiments, theclient application 1920 and/or server components will be integrated intothe stimulation device 1930 (e.g., stored in a memory and operable onthe device controller 1932) instead of residing on a separate computingdevice.

The stimulation device 1930 may be any device configured to providesexual stimulation by any variety of means, including but not limitedto, linear stroking, vibration, rotation, heat, electrical stimulation,or combinations of the above. Device sensors 1931 may be any sensor onthe device capable of providing data regarding an aspect of sexualarousal, including but not limited to, heartrate sensors, moisturesensors, tumescence sensors, pressure sensors, strain gauges, andlength/distance sensors. Further, the device sensors 1931 includedevices capable of receiving sensor data from external sensors (e.g.,wearable fitness devices that record heart rates) via WiFi, Bluetooth,or other networking technologies. The device controller 1932 is a devicecapable of operating the stimulation device based on control signalsreceived. The device controller 1932 may be a simple power relayswitching device that receives low-powered signals and outputscorresponding power to motors, vibrators, etc., or may be a computingdevice with a memory, processor, and storage. In the latter case, thedevice controller 1932 may be configured to receive instructions togenerate control signals and generate the control signals, itself.Further, in some embodiments, aspects of the client application and/ormachine learning algorithm 1912 may be incorporated into the devicecontroller 1932.

FIG. 20 is an exemplary algorithm for an automated set-up wizard for asystem for automated control of sexual stimulation devices. The set-upwizard application 1922 builds an initial personalized stimulationprofile from a series of ratings by the user of test stimulations.Completion of the set-up wizard application 1922 process acceleratescustomization of a stimulation profile for the user by providing a baseset of ratings of various aspects of stimulation which can then beprocessed through the trained machine learning algorithm 1912 toautomatically control the stimulation device 1930. After completion ofthe set-up wizard application 1922, stimulation profiles for the usermay continue to evolve from new user-specific data as described above.

In this embodiment, the set-up wizard application 1922 process has twostages, an analysis stage and a testing stage. At the analysis stage2010 stimulation selections are made from a set of pre-programmedaspects such as tempo, location, and pattern, and the user's ratings foreach selection are used by the machine learning algorithm 1912 togenerate a stimulation routine comprising one or more tempos, locations,and patterns of stimulation. At the testing stage 2020, stimulation isperformed using the generated stimulation routine, and the generatedstimulation routine is refined through ratings by the user and,optionally, introduction of variations deemed likely to improve thoseratings. Optionally, the generated stimulation routine may be displayedon a user interface such as that shown in FIG. 21 , and additionalrefinements may be made by manual adjustments to the routine by the userusing the user interface.

In this exemplary process, the process begins at the analysis stage 2010with the system's selection of one or more tempos of stimulation 2011from a set of pre-programmed (or randomly chosen) and user ratings 2012for each selected tempo. On each attempt, the tempo is changed and a newrating is obtained. For example, if the system selects a slow tempo, andthe user gives it a low rating, the system may select a faster tempo forthe next selection and rating. Once a tempo, or range of tempos, isestablished, the system goes through the same process for location 2013and user ratings associated with location 2014 using that tempo, andagain with patterns of stimulation 2015 and user ratings 2016 basedaround the established tempo and established location. For a devicecapable of producing linear stroking motions, the patterns ofstimulation may include, but are not limited to, variations in theestablished tempo, variations in the established location, stopping orstarting of stimulation at various timings, and stimulation outside ofthe established tempo and established location for a period of timebefore returning to them. The user's ratings of the tempo, location, andpatterns of stimulation are processed through the machine learningalgorithm 1912 to generate one or more test stimulation routines 2017for testing. At the testing stage 2020, a routine is selected 2021 fromthe one or more test stimulation routines 2017 and rated by the user2022. This process may be repeated for several test stimulation routines2017. In some cases (for example when only a single test stimulationroutine is generated or where the test routines are all rated poorly bythe user), the system may introduce variations in one or more of thetest routines 2023 in an attempt to increase the user's rating 2024 ofthat test routine. The variations come from any number of sources,including but not limited to, a list of known variations, variationsgenerated by the machine learning algorithm 1912, and random variation.Once the testing stage 2020 is completed, one or more preferredstimulation routines are stored, along with the analysis and testingdata for future use 2025.

FIG. 21 is an exemplary screenshot of a user interface for viewing,adjustment, and rating of automated control settings for a sexualstimulation device. During manual operation of the stimulation device1930, various aspects of the current stimulation being provided by thestimulation device 1930 are displayed on an appropriate display orcomputing device, and the controls for each aspect may be adjusted bythe user according to preference. During automated operation of thestimulation device 1930, various aspects of the operation of thestimulation device 1930 are displayed, reflecting the currentstimulation routine. Each of the aspects displayed can be changed by theuser to manually override the current settings, and themanually-overridden settings will be provided to the client application1930 or server 1910 for adjustment of the current stimulation routineaccording (and for evolution of that user's preferred stimulationroutines). During the set-up wizard application 1922 process, thesedisplays and controls 2110-2160 may be used to adjust and rate theaspect of stimulation under test.

In this example, it is assumed that the current stimulation routine isbeing displayed on a mobile phone or tablet device with a touch screen,although the system is not so limited. In this screenshot, a temposelector 2110 is shown with an arrow indicating the current tempo ofstimulation on a range from minimum to maximum. The tempo arrow can bemoved by the user to override the tempo setting of the currentstimulation routine, and the override information will be forwarded tothe client application or server 1910 for adjustment of the currentstimulation routine and evolution of the user's stimulation preferencesover time. A location selector 2120 is shown with an slider 2121indicating the current location of stimulation (here on a device thatprovides stimulation using a reciprocal linear motion). The slider 2121can be moved by the user to override the location setting of the currentstimulation routine, and the override information will be forwarded tothe client application or server 1910 for adjustment of the currentstimulation routine and evolution of the user's stimulation preferencesover time. At the location indicated by the slider 2121, a powerselector 2130 displays the current power setting for that location andallows the user to adjust the power setting for that location, and apattern selector 2140 displays the current pattern setting for thatlocation and allows the user to adjust the pattern setting for thatlocation. A different position of the slider is shown at 2150, alongwith the power selector 2130 and pattern selector 2140 for thatdifferent location. A rating bar 2160 is shown at the bottom of thescreen, allowing the user to input a rating for the current stimulation.

FIG. 22 is an exemplary system architecture diagram for a system forthought-based control of sexual stimulation devices. In this embodiment,the system comprises a server 2210, an electroencephalograph (EEG)training and control application 2300, an EEG headset, one or more otherbiometric sensors, a stimulation device 2230, and data from other usersand EEG devices 2240.

The server may be a network-connected, cloud-based, or local server2210, and comprises a database 2211 for storage of user data comprisingEEG brain activity patterns and control setting associations 2211, and amachine learning algorithm 2212 for analysis of the data stored in thedatabase 2211 for generation of thought-based control signals orinstructions. The machine learning algorithm 2212 is trained on the datato identify patterns within the usage data wherein certain EEG patternsare correlated with stimulation device controls and/or biometric sensordata. The user data may further contain user profiles comprisingpersonal information about the user such as age, sex, height, weight,and fitness level; sexual preferences such as straight, gay, bi-sexual,etc.; stimulation preferences such as stimulation tempo/speed,stimulation intensity, location of stimulation, patterns of stimulation;and feedback information such as user ratings, other biometric sensordata such as heartrate data from sensors, moisture data from sensors,etc; all of which may be incorporated by the machine learning algorithmto better correlate EEG patterns with stimulation device controls forspecific users. After training, when an EEG pattern from the EEG headsetis input into the machine learning algorithm 2212, the machine learningalgorithm 2212 generates one or more control signals or instructions forthe stimulation device 2230 based on the associations between EEGpatterns and control settings learned by the machine learning algorithmduring training. The machine learning algorithm 2212 may periodically orcontinuously be re-trained based on new data from theelectroencephalograph (EEG) training and control application 2300 (suchas, but not limited to, new training data acquired as a result ofadditional EEG training by the user) and the data from other users andEEG devices 2240 being similarly stored and processed. It should benoted that, while a machine learning algorithm is used in embodiment,the system is not necessarily limited to use of machine learningalgorithms and other processes for analysis of the data may be used,including but not limited to modeling and statistical calculations. Forexample, in some embodiments, the machine learning aspect may bebypassed altogether, having the system rely only on EEG pattern/controlsignal associations from the user-specific training conducted by the EEGtraining & control application 2300. In other embodiments, a two-stagetraining algorithm may be used wherein the machine learning algorithm isfirst trained generically on a large number of users, then re-trainedfor a particular user using user-specific training data. In someembodiments, control signals for the stimulation device may be based ona combination of non-machine learning algorithm EEG pattern/controlsignal associations and machine learning algorithm EEG pattern/controlsignal associations.

The system of this embodiment further comprises an electroencephalograph(EEG) training and control application 2300, which is a softwareapplication operating on a computing device, which may be of any typeincluding but not limited to a desktop computer, tablet, mobile phone,or even a cloud-based server accessible via a web browser. Theelectroencephalograph (EEG) training and control application 2300 actsas an interface between the stimulation device 2230, the machinelearning algorithm 2212, and the EEG headset 2500 and other biometricsensors 2222, as well as operating to train the system to makeassociations between EEG patterns and control signals for a particularuser or users. In its role as an interface, the EEG training and controlapplication 2300 relays feedback from the device to the server 2210 andrelays control signals (or translates instructions into control signals)to the device controller 2232 of the stimulation device 2230. Detailsregarding the architecture and operation of the EEG training and controlapplication 2300 are further described below. Depending onconfiguration, the electroencephalograph (EEG) training and controlapplication 2300 may further act as a user interface for operation of,and/or changing settings of, the stimulation device 2230. In its role asan EEG training application, the EEG training and control application2300 assigns training tasks to the user, receives EEG signal datacomprising measurements of electrical activity in parts of the user'sbrain from the EEG headset 2500, and associates patterns of EEG signaldata with objectives of the training tasks (e.g., think about moving anon-screen control downward, corresponding to a reduction in the speed orintensity of operation of the stimulation device).

In this embodiment, the EEG headset 2500 is worn by a user and sends EEGsignal data from electrodes of the EEG headset to the EEG training &control application 2300. The user data may further comprise biometricsignals data from other biometric sensors 2222. EEG signal data is aform of biometric data, but other biometric sensors 2222 may be used toprovide biometric signal data that is not associated with brainactivity, such as external or third-party heartrate monitors thatprovide heartrate data.

The EEG training and control application 2300 provides the user-specificdata comprising EEG patterns, or control associations, or both to theserver 2210 and requests control signals (or instructions for controlsignals) for the stimulation device 2230 based on the user-specificdata. During training of the machine learning algorithm, the EEGpatterns and control associations are used as a form of labeled trainingdata to train or re-train the machine learning algorithm 2212. Aftertraining, the EEG patterns may be processed through the trained machinelearning algorithm 2212, which provides control signals or instructionsback to the electroencephalograph (EEG) training and control applicationfor operation of the stimulation device 2230. In some embodiments, theEEG patterns are sent to the machine learning algorithm 2212 andprocessed into control signals in real time or near real time. In someembodiments the control signals or instructions may be sent directlyfrom the machine learning algorithm 2212 directly to the devicecontroller 2232 of the stimulation device 2230. Theelectroencephalograph (EEG) training and control application 2300 may beconfigured to periodically or continuously send updated user-specificdata to the server 2210 for processing by the machine learning algorithm2212 to generate modified or updated control signals or instructions,thus changing and evolving the automated operation of the device basedon changed or updated information from the device sensors 2231,electroencephalograph (EEG) training and control application 2300, orupdating/retraining of the machine learning algorithm 2212 based on theuser's data and the data from other users and EEG devices 2240 beingsimilarly stored and processed.

In some embodiments, the electroencephalograph (EEG) training andcontrol application 2300 may exist as an application on a user's mobilephone, and may interface with the stimulation device 2230 via a localnetwork (e.g., WiFi, Bluetooth, etc.). In other embodiments, theelectroencephalograph (EEG) training and control application 2300 mayexist as an application on the server 2300 accessible via a user accountalso residing on the server. In other embodiments, certain components ofthe server 2210 and electroencephalograph (EEG) training and controlapplication 2300 may reside on tablet computer or other mobile device,or on the stimulation device 2230 itself (e.g., a copy of the trainedmachine learning algorithm could reside on a smartphone such thatautomated generation of control signals can be accomplished withoutaccess to the server). In some embodiments, the electroencephalograph(EEG) training and control application 2300 and/or server componentswill be integrated into the stimulation device 2230 (e.g., stored in amemory and operable on the device controller 2232) instead of residingon a separate computing device.

The stimulation device 2230 may be any device configured to providesexual stimulation by any variety of means, including but not limitedto, linear stroking, vibration, rotation, heat, electrical stimulation,or combinations of the above. Device sensors 2231 may be any sensor onthe device capable of providing data regarding an aspect of sexualarousal, including but not limited to, heartrate sensors, moisturesensors, tumescence sensors, pressure sensors, strain gauges, andlength/distance sensors. Further, the device sensors 2231 includedevices capable of receiving sensor data from external sensors (e.g.,wearable fitness devices that record heart rates) via WiFi, Bluetooth,or other networking technologies. The device controller 2232 is a devicecapable of operating the stimulation device based on control signalsreceived. The device controller 2232 may be a simple power relayswitching device that receives low-powered signals and outputscorresponding power to motors, vibrators, etc., or may be a computingdevice with a memory, processor, and storage. In the latter case, thedevice controller 2232 may be configured to receive instructions togenerate control signals and generate the control signals, itself.Further, in some embodiments, aspects of the electroencephalograph (EEG)training and control application and/or machine learning algorithm 2212may be incorporated into the device controller 2232.

FIG. 23 is an exemplary system architecture diagram for anelectroencephalograph training and control application aspect of asystem for thought-based control of sexual stimulation devices. Theelectroencephalograph (EEG) training and control application 2300 is asoftware application operating on a computing device, which may be ofany type including but not limited to a desktop computer, tablet, mobilephone, or even a cloud-based server accessible via a web browser. TheEEG training and control application 2300 acts as an interface betweenthe stimulation device 2230, the machine learning algorithm 2212, andthe EEG headset 2500 and other biometric sensors 2222, as well asoperating to train the system to make associations between EEG patternsand control signals for a particular user or users. In its role as aninterface, the EEG training and control application 2300 relays feedbackfrom the device to the server 2210 and relays control signals (ortranslates instructions into control signals) to the device controller2232 of the stimulation device 2230. Details regarding the architectureand operation of the EEG training and control application 2300 arefurther described below. Depending on configuration, the EEG trainingand control application 2300 may further act as a user interface foroperation of, and/or changing settings of, the stimulation device 2230.In its role as an EEG training application, the EEG training and controlapplication 2300 assigns training tasks to the user, receives EEG signaldata comprising measurements of electrical activity in parts of theuser's brain from the EEG headset 2500, and associates patterns of EEGsignal data with objectives of the training tasks (e.g., think aboutmoving an on-screen control downward, corresponding to a reduction inthe speed or intensity of operation of the stimulation device). The EEGtraining and control application 2300 of this embodiment comprises anEEG data manager 2301, a graphical display manager 2302, a controlsignal generator 2303, a training data labeler 2304, and threedatabases, an EEG pattern storage database 2305, an EEG training tasklibrary 2306, and a stimulation routine library 2307.

Depending on its configuration, the EEG data manager 2301 is responsiblefor generation of labeled training data to the machine learningalgorithm for supervised learning, pass-through of EEG signal data tothe machine learning algorithm for unsupervised learning, receipt ofcontrol signals from the trained machine learning algorithm based onpass-through of EEG signal data, or generating control signals by directassociation of EEG patterns with objectives corresponding to devicecontrols, or any combination of the above. In this embodiment, it isassumed that the EEG data manager is configured to generate EEGpattern/objective pairs either to directly generate control signalsitself, or to pass those EEG pattern/objective pairs to the machinelearning algorithm for training. In other configurations, however, theEEG data manager may pass through EEG signal data to the machinelearning algorithm for unsupervised learning in which the machinelearning algorithm identifies the EEG patterns and makes associationswith the objectives. In cases involving complex and/or voluminous datasuch as detecting patterns in EEG signal data, unsupervised learning isoften useful in that it can find hidden or difficult-to-identifypatterns in the data that might otherwise be missed.

The EEG data manager 2301 retrieves and implements EEG training tasksfrom the EEG training task library 2306. The training tasks comprise astimulus such as auditory, visual cues, or sexual stimulation, anobjective such as moving a virtual slider displayed on a screen, andinstructions for the user to attempt to achieve the objective using somemental image or thought. For example, a training task may involvedisplaying a task on a visual display using the graphical displaymanager, wherein the display shows a vertical sliding controller and theinstructions may instruct the user to think about moving the verticalsliding controller upward (representing increased speed or intensity ofsome aspect of the stimulation device) or downward (representingdecreased speed or intensity of some aspect of the stimulation device).While the user is performing the task, the EEG headset 2500 detectselectrical signals representing brain activity of the user underneatheach electrode and forwards those electrical signals as EEG signal datato the EEG data manager 2301. The EEG data manager 2301 receives EEGsignal data from the EEG headset 2500 and identifies a pattern of EEGactivity from the EEG signal data. The pattern of EEG activity (aka anEEG pattern) may be a spatial pattern (i.e., differences in electricalsignals among electrodes spaced across the user's head), a temporalpattern (i.e., changes in the electrical signal in each electrode overtime), or both. The EEG data manager 2301 associates the EEG pattern orpatterns with an objective of the task (e.g., moving of the verticalcontrol slider downward), creating EEG pattern/objective pairs that canbe used either to generate controls for the stimulation device via acontrol signal generator 2303 or as labeled training data via a trainingdata labeler 2304. The EEG pattern/objective pairs may be stored in theEEG pattern storage database 2305. In some embodiments, new EEGpattern/objective pairs may be compared with stored EEGpattern/objective pairs to confirm, reject, or modify associations.

In some embodiments, the stimulus for some EEG training tasks maycomprise stimulation via the stimulation device as a supplement toauditory or visual tasks, or as an alternative thereto. The EEG datamanager 2301 may select one or more stimulation routines from astimulation routine library 2307, apply the stimulation to the user viathe stimulation device 2230, and have the user think about an objectiverelated to the stimulation. For example, the EEG data manager 2301 mayinitiate stimulation at a low speed or intensity, and ask the user tothink about increasing the stimulation speed or intensity. In somecases, the objective may simply be free association of the stimulationwith certain of the user's thoughts. Similarly to the EEG training forauditory and visual tasks, the EEG data manager 2301 associates the EEGpattern or patterns with an objective of the stimulation (e.g.,increasing the speed or intensity of stimulation), creating EEGpattern/objective pairs that can be used either to generate controls forthe stimulation device via a control signal generator 2303 or as labeledtraining data via a training data labeler 2304. The EEGpattern/objective pairs may be stored in the EEG pattern storagedatabase 2305. In some embodiments, new EEG pattern/objective pairs maybe compared with stored EEG pattern/objective pairs to confirm, reject,or modify associations.

In some embodiments, the associations may further incorporate biometricsignal data from other biometric sensors 2222, creating more complexassociations which may be stored as tables, high dimensional vectors,graphs, or other forms of complex relationship storage. In some cases,the user may provide additional user feedback via the graphical displaymanager 2302 by interacting with the display. Such user feedback may be,for example, indicating a level of concentration the user was able toapply, a mood of the user, or a tiredness level of the user, which userfeedback may be used as additional association information.

The more complex the association data between EEG patterns, tasks,feedback, and stimulation routines, the more useful the machine learningalgorithm 2212 is in determining relationships between the input data(e.g., EEG signals, biometric signals, user feedback) and the intendedoutputs (i.e., control of some aspect of the stimulation device).

FIG. 24 is an exemplary algorithm for electroencephalograph data captureand machine learning algorithm training for thought-based control ofsexual stimulation devices. This exemplary algorithm comprises threestages, training on curated data 2410, capture of, and training on,user-specific data using visual tasks 2420, and capture of, and trainingon, user-specific data using stimulation tasks 2430. While in thisexemplary algorithm the stages are shown as sequential, in someembodiments these and other stages or training could be usedindividually or in other combinations.

Stage 1 of this embodiment comprises training the machine learningalgorithm generically (i.e., for a typical, unspecified user) usingpre-labeled data from other users 2411 who have performed EEG trainingtasks using their own EEG devices. This pre-labeled training data doesnot necessarily have to be in the field of control of sexual stimulationdevices, and may be pre-labeled training data from control of otherdevices or performance of other tasks (e.g., biofeedback relaxationroutines, meditation, etc.), as long as there is some association in thepre-labeled data between EEG patterns and some objective that could betranslated or applied to control of devices.

Stage 2 of this embodiment comprises user-specific EEG training usingvisual tasks 2420. A visual EEG training task is selected and displayedon a display of a computing device 2421. The training task comprisesvisual cues with instructions for the user to associate the visual cueswith some mental image or thought. For example, the training task mayinvolve displaying a task on a computer screen or other visual displayof a computing device, wherein the display shows a vertical slidingcontroller and the instructions may instruct the user to think aboutmoving the vertical sliding controller upward (representing increasedspeed or intensity of some aspect of the stimulation device) or downward(representing decreased speed or intensity of some aspect of thestimulation device). While the user is performing the task, an EEGheadset 2500 detects electrical signals representing brain activity ofthe user underneath each electrode and forwards those electrical signalsas EEG signal data, which is received and recorded 2422. The visualdisplay is updated with progress of the user in accomplishing the task(for example, where the user's EEG patterns match expected EEG patternsstored in the EEG pattern storage database 2300) or simply updated withan impression of progress designed to encourage the user to continueexhibiting the same EEG patterns 2423. The EEG patterns are associatedwith the task objective 2424. The pattern of EEG activity (aka an EEGpattern) may be a spatial pattern (i.e., differences in electricalsignals among electrodes spaced across the user's head), a temporalpattern (i.e., changes in the electrical signal in each electrode overtime), or both. The EEG data manager 2301 associates the EEG pattern orpatterns with an objective of the task (e.g., moving of the verticalcontrol slider downward), creating EEG pattern/objective pairs that canbe used either to generate controls for the stimulation device 2425 oras labeled training data for use in training a machine learningalgorithm 2440. The EEG pattern/objective pairs may be stored in an EEGpattern storage database 2305. In some embodiments, new EEGpattern/objective pairs may be compared with stored EEGpattern/objective pairs to confirm, reject, or modify associations. Theprocess may be repeated until a desired quantity of data is obtained.

Stage 3 of this embodiment comprises user-specific EEG training usingstimulation tasks 2430, comprising stimulation via the stimulationdevice. A stimulation routine is selected from a stimulation routinelibrary 2307, applied to the user via the stimulation device 2230, andthe user is asked to think about an aspect of the stimulation or makesome other mental association with the stimulation (e.g., an image,feeling, etc.) 2431. For example, the stimulation may be initiated at alow speed or intensity, and the user may be asked to think aboutincreasing the stimulation speed or intensity. EEG signal data from theEEG headset 2500 is received and recorded 2432. Optionally, biometricsignal data and/or user feedback may additionally be received andrecorded 2433. Similarly to the EEG training for visual tasks, the EEGpattern or patterns are associated with an objective of the stimulation2434 (e.g., increasing the speed or intensity of stimulation), creatingEEG pattern/objective pairs that can be used either to generate controlsfor the stimulation device 2425 or as labeled training data for use intraining a machine learning algorithm 2440. The EEG pattern/objectivepairs may be stored in an EEG pattern storage database 2305. In someembodiments, new EEG pattern/objective pairs may be compared with storedEEG pattern/objective pairs to confirm, reject, or modify associations.The process may be repeated until a desired quantity of data isobtained.

The more complex the association data between EEG patterns, tasks,feedback, and stimulation routines, the more useful the machine learningalgorithm 2212 is in determining relationships between the input data(e.g., EEG signals, biometric signals, user feedback) and the intendedoutputs (i.e., control of some aspect of the stimulation device).

FIG. 25 is an exemplary illustration of an electroencephalograph (EEG)headset for use in thought-based control of sexual stimulation devices.An EEG headset 2500 is a device intended to be worn on the head of aperson which places electrodes on the person's head for the purpose ofmeasuring electrical signals generated by the brain underneath thelocation of each electrode. This exemplary illustration shows an EEGheadset 2500 in a top-down view (i.e., from above the head of a personwearing the EEG headset).

In this embodiment, the EEG headset 2500 comprises a frame 2510, ainterface 2520, and a plurality of electrodes 2530. The frame comprisesside rails 2511 configured to rest horizontally along the side of theperson's head just above the ears, a rear rail 2522 configured to resthorizontally along the back of the person's head, a top rail 2513configured to rest horizontally along the top of the person's head, anda forehead extension 2514. The electrodes 2530 in this embodiment areall circular electrodes as shown at ref 2533, but some are shown inoblique perspective 2532 or side perspective 2531 as they progress downthe sides of the person's head from the top. The electrodes areconfigured to be lightly pressed against the person's head while in use,ideally as close to the person's scalp as possible to maximize signalcapture. Electrical signals from brain activity received by electrodesare small and will typically be in the 1 microvolt (1 μV) to 10microvolt (10 μV) range. The electrodes are shown in this diagram in theInternational 10-20 placement system which is the standardized EEGelectrode placement of the International Federation of ClinicalNeurophysiology (IFCN). Other electrode placement patterns are possible.Many other arrangements, configurations, materials of the EEG headsetare possible, including frameless and controller-less configurations,configurations in which the frame is mesh-based, net-based orstrap-based, frameless configurations in which the electrodes are heldin place on the head using an adhesive, so long as, when in use, atleast one electrode is held on or near the scalp of the person using theEEG headset such that electrical activity in the person's brainunderneath the scalp can be received by the electrode and stored ortransmitted. In some configurations, the storage and transmission mayoccur to a computing device on or within the EEG headset, itself.

The interface 2520 is electrically connected to the electrodes, andprovides a means for transmission of the electrical signals from theelectrodes to other devices. The interface may have a case 2521containing electronics or may be fully integrated into the frame 2510 ofthe EEG headset 2500. The interface may contain electronics that receiveand convert the signals before transmission (e.g., analog to digitalconversion) or may simply pass through the raw electrical signals. Theinterface may transmit electrical signals via a wired connection 2522 orvia a wireless transmitter (not shown).

FIG. 26 is an exemplary illustration electroencephalograph sensorplacement and data patterns of an electroencephalograph headset for usein thought-based control of sexual stimulation devices. The electrodesare shown in this diagram in the International 10-20 placement systemwhich is the standardized EEG electrode placement of the InternationalFederation of Clinical Neurophysiology (IFCN). Other electrode placementpatterns are possible. Here, the sensors are also shown grouped intofunctional areas of the brain including the frontal lobe area associatedwith reasoning, speech, emotions, and problem-solving 2611, mid-brainareas associated with sensorimotor functions 2612 and attention,perception, and processing of sense stimuli 2613, lower brain areasassociated with memory and auditory functions 2614, 2615, and rear brainareas associated with visual functions 2616.

The lefthand drawing 2610 shows the orientation of the user's head withelectrodes 2618 a-n placed according to the International 10-20placement system within the various functional areas 2611-2616. Therighthand drawing 2620 shows the same orientation and electrodeplacement, but illustrates a possible spatial EEG pattern of electricalactivity in the user's brain. The darker borders of the electrodes showincreased levels of activity in certain areas of the brain such as areaswhere there is little or no electrical activity 2621, areas where thereis low electrical activity 2622, areas where there is a moderate levelof electrical activity 2623, and areas where there is a high level ofelectrical activity 2624. These spatial EEG patterns may be associatedwith task objectives such as increasing or decreasing the speed orintensity of a controller for a stimulation device. Temporal EEGpatterns (i.e., changes in one or more electrodes over time) may also beassociated with task objectives.

Hardware Architecture

Generally, the techniques disclosed herein may be implemented onhardware or a combination of software and hardware. For example, theymay be implemented in an operating system kernel, in a separate userprocess, in a library package bound into network applications, on aspecially constructed machine, on an application-specific integratedcircuit (ASIC), or on a network interface card.

Software/hardware hybrid implementations of at least some of the aspectsdisclosed herein may be implemented on a programmable network-residentmachine (which should be understood to include intermittently connectednetwork-aware machines) selectively activated or reconfigured by acomputer program stored in memory. Such network devices may havemultiple network interfaces that may be configured or designed toutilize different types of network communication protocols. A generalarchitecture for some of these machines may be described herein in orderto illustrate one or more exemplary means by which a given unit offunctionality may be implemented. According to specific aspects, atleast some of the features or functionalities of the various aspectsdisclosed herein may be implemented on one or more general-purposecomputers associated with one or more networks, such as for example anend-user computer system, a client computer, a network server or otherserver system, a mobile computing device (e.g., tablet computing device,mobile phone, smartphone, laptop, or other appropriate computingdevice), a consumer electronic device, a music player, or any othersuitable electronic device, router, switch, or other suitable device, orany combination thereof. In at least some aspects, at least some of thefeatures or functionalities of the various aspects disclosed herein maybe implemented in one or more virtualized computing environments (e.g.,network computing clouds, virtual machines hosted on one or morephysical computing machines, or other appropriate virtual environments).

Referring now to FIG. 27 , there is shown a block diagram depicting anexemplary computing device 10 suitable for implementing at least aportion of the features or functionalities disclosed herein. Computingdevice 10 may be, for example, any one of the computing machines listedin the previous paragraph, or indeed any other electronic device capableof executing software- or hardware-based instructions according to oneor more programs stored in memory. Computing device 10 may be configuredto communicate with a plurality of other computing devices, such asclients or servers, over communications networks such as a wide areanetwork a metropolitan area network, a local area network, a wirelessnetwork, the Internet, or any other network, using known protocols forsuch communication, whether wireless or wired.

In one aspect, computing device 10 includes one or more centralprocessing units (CPU) 12, one or more interfaces 15, and one or morebusses 14 (such as a peripheral component interconnect (PCI) bus). Whenacting under the control of appropriate software or firmware, CPU 12 maybe responsible for implementing specific functions associated with thefunctions of a specifically configured computing device or machine. Forexample, in at least one aspect, a computing device 10 may be configuredor designed to function as a server system utilizing CPU 12, localmemory 11 and/or remote memory 16, and interface(s) 15. In at least oneaspect, CPU 12 may be caused to perform one or more of the differenttypes of functions and/or operations under the control of softwaremodules or components, which for example, may include an operatingsystem and any appropriate applications software, drivers, and the like.

CPU 12 may include one or more processors 13 such as, for example, aprocessor from one of the Intel, ARM, Qualcomm, and AMD families ofmicroprocessors. In some aspects, processors 13 may include speciallydesigned hardware such as application-specific integrated circuits(ASICs), electrically erasable programmable read-only memories(EEPROMs), field-programmable gate arrays (FPGAs), and so forth, forcontrolling operations of computing device 10. In a particular aspect, alocal memory 11 (such as non-volatile random access memory (RAM) and/orread-only memory (ROM), including for example one or more levels ofcached memory) may also form part of CPU 12. However, there are manydifferent ways in which memory may be coupled to system 10. Memory 11may be used for a variety of purposes such as, for example, cachingand/or storing data, programming instructions, and the like. It shouldbe further appreciated that CPU 12 may be one of a variety ofsystem-on-a-chip (SOC) type hardware that may include additionalhardware such as memory or graphics processing chips, such as a QUALCOMMSNAPDRAGON™ or SAMSUNG EXYNOS™ CPU as are becoming increasingly commonin the art, such as for use in mobile devices or integrated devices.

As used herein, the term “processor” is not limited merely to thoseintegrated circuits referred to in the art as a processor, a mobileprocessor, or a microprocessor, but broadly refers to a microcontroller,a microcomputer, a programmable logic controller, anapplication-specific integrated circuit, and any other programmablecircuit.

In one aspect, interfaces 15 are provided as network interface cards(NICs). Generally, NICs control the sending and receiving of datapackets over a computer network; other types of interfaces 15 may forexample support other peripherals used with computing device 10. Amongthe interfaces that may be provided are Ethernet interfaces, frame relayinterfaces, cable interfaces, DSL interfaces, token ring interfaces,graphics interfaces, and the like. In addition, various types ofinterfaces may be provided such as, for example, universal serial bus(USB), Serial, Ethernet, FIREWIRE™ THUNDERBOLT™, PCI, parallel, radiofrequency (RF), BLUETOOTH™, near-field communications (e.g., usingnear-field magnetics), 802.11 (WiFi), frame relay, TCP/IP, ISDN, fastEthernet interfaces, Gigabit Ethernet interfaces, Serial ATA (SATA) orexternal SATA (ESATA) interfaces, high-definition multimedia interface(HDMI), digital visual interface (DVI), analog or digital audiointerfaces, asynchronous transfer mode (ATM) interfaces, high-speedserial interface (HSSI) interfaces, Point of Sale (POS) interfaces,fiber data distributed interfaces (FDDIs), and the like. Generally, suchinterfaces 15 may include physical ports appropriate for communicationwith appropriate media. In some cases, they may also include anindependent processor (such as a dedicated audio or video processor, asis common in the art for high-fidelity A/V hardware interfaces) and, insome instances, volatile and/or non-volatile memory (e.g., RAM).

Although the system shown in FIG. 27 illustrates one specificarchitecture for a computing device 10 for implementing one or more ofthe aspects described herein, it is by no means the only devicearchitecture on which at least a portion of the features and techniquesdescribed herein may be implemented. For example, architectures havingone or any number of processors 13 may be used, and such processors 13may be present in a single device or distributed among any number ofdevices. In one aspect, a single processor 13 handles communications aswell as routing computations, while in other aspects a separatededicated communications processor may be provided. In various aspects,different types of features or functionalities may be implemented in asystem according to the aspect that includes a client device (such as atablet device or smartphone running client software) and server systems(such as a server system described in more detail below).

Regardless of network device configuration, the system of an aspect mayemploy one or more memories or memory modules (such as, for example,remote memory block 16 and local memory 11) configured to store data,program instructions for the general-purpose network operations, orother information relating to the functionality of the aspects describedherein (or any combinations of the above). Program instructions maycontrol execution of or comprise an operating system and/or one or moreapplications, for example. Memory 16 or memories 11, 16 may also beconfigured to store data structures, configuration data, encryptiondata, historical system operations information, or any other specific orgeneric non-program information described herein.

Because such information and program instructions may be employed toimplement one or more systems or methods described herein, at least somenetwork device aspects may include nontransitory machine-readablestorage media, which, for example, may be configured or designed tostore program instructions, state information, and the like forperforming various operations described herein. Examples of suchnontransitory machine-readable storage media include, but are notlimited to, magnetic media such as hard disks, floppy disks, andmagnetic tape; optical media such as CD-ROM disks; magneto-optical mediasuch as optical disks, and hardware devices that are speciallyconfigured to store and perform program instructions, such as read-onlymemory devices (ROM), flash memory (as is common in mobile devices andintegrated systems), solid state drives (SSD) and “hybrid SSD” storagedrives that may combine physical components of solid state and hard diskdrives in a single hardware device (as are becoming increasingly commonin the art with regard to personal computers), memristor memory, randomaccess memory (RAM), and the like. It should be appreciated that suchstorage means may be integral and non-removable (such as RAM hardwaremodules that may be soldered onto a motherboard or otherwise integratedinto an electronic device), or they may be removable such as swappableflash memory modules (such as “thumb drives” or other removable mediadesigned for rapidly exchanging physical storage devices),“hot-swappable” hard disk drives or solid state drives, removableoptical storage discs, or other such removable media, and that suchintegral and removable storage media may be utilized interchangeably.Examples of program instructions include both object code, such as maybe produced by a compiler, machine code, such as may be produced by anassembler or a linker, byte code, such as may be generated by forexample a JAVA™ compiler and may be executed using a Java virtualmachine or equivalent, or files containing higher level code that may beexecuted by the computer using an interpreter (for example, scriptswritten in Python, Perl, Ruby, Groovy, or any other scripting language).

In some aspects, systems may be implemented on a standalone computingsystem. Referring now to FIG. 28 , there is shown a block diagramdepicting a typical exemplary architecture of one or more aspects orcomponents thereof on a standalone computing system. Computing device 20includes processors 21 that may run software that carry out one or morefunctions or applications of aspects, such as for example a clientapplication 24. Processors 21 may carry out computing instructions undercontrol of an operating system 22 such as, for example, a version ofMICROSOFT WINDOWS™ operating system, APPLE macOS™ or iOS™ operatingsystems, some variety of the Linux operating system, ANDROID™ operatingsystem, or the like. In many cases, one or more shared services 23 maybe operable in system device 20, and may be useful for providing commonservices to client applications 24. Services 23 may for example beWINDOWS™ services, user-space common services in a Linux environment, orany other type of common service architecture used with operating system21. Input devices 28 may be of any type suitable for receiving userinput, including for example a keyboard, touchscreen, microphone (forexample, for voice input), mouse, touchpad, trackball, or anycombination thereof. Output devices 27 may be of any type suitable forproviding output to one or more users, whether remote or local to device20, and may include for example one or more screens for visual output,speakers, printers, or any combination thereof. Memory 25 may berandom-access memory having any structure and architecture known in theart, for use by processors 21, for example to run software. Storagedevices 26 may be any magnetic, optical, mechanical, memristor, orelectrical storage device for storage of data in digital form (such asthose described above, referring to FIG. 27 ). Examples of storagedevices 26 include flash memory, magnetic hard drive, CD-ROM, and/or thelike.

In some aspects, systems may be implemented on a distributed computingnetwork, such as one having any number of clients and/or servers.Referring now to FIG. 29 , there is shown a block diagram depicting anexemplary architecture 30 for implementing at least a portion of asystem according to one aspect on a distributed computing network.According to the aspect, any number of clients 33 may be provided. Eachclient 33 may run software for implementing client-side portions of asystem; clients may comprise a device 20 such as that illustrated inFIG. 28 . In addition, any number of servers 32 may be provided forhandling requests received from one or more clients 33. Clients 33 andservers 32 may communicate with one another via one or more electronicnetworks 31, which may be in various aspects any of the Internet, a widearea network, a mobile telephony network (such as CDMA or GSM cellularnetworks), a wireless network (such as WiFi, WiMAX, LTE, and so forth),or a local area network (or indeed any network topology known in theart; the aspect does not prefer any one network topology over anyother). Networks 31 may be implemented using any known networkprotocols, including for example wired and/or wireless protocols.

In addition, in some aspects, servers 32 may call external services 37when needed to obtain additional information, or to refer to additionaldata concerning a particular call. Communications with external services37 may take place, for example, via one or more networks 31. In variousaspects, external services 37 may comprise web-enabled services orfunctionality related to or installed on the hardware device itself. Forexample, in one aspect where client applications 24 are implemented on asmartphone or other electronic device, client applications 24 may obtaininformation stored in a server system 32 in the cloud or on an externalservice 37 deployed on one or more of a particular enterprise's oruser's premises. In addition to local storage on servers 32, remotestorage 38 may be accessible through the network(s) 31.

In some aspects, clients 33 or servers 32 (or both) may make use of oneor more specialized services or appliances that may be deployed locallyor remotely across one or more networks 31. For example, one or moredatabases 34 in either local or remote storage 38 may be used orreferred to by one or more aspects. It should be understood by onehaving ordinary skill in the art that databases in storage 34 may bearranged in a wide variety of architectures and using a wide variety ofdata access and manipulation means. For example, in various aspects oneor more databases in storage 34 may comprise a relational databasesystem using a structured query language (SQL), while others maycomprise an alternative data storage technology such as those referredto in the art as “NoSQL” (for example, HADOOP CASSANDRA™, GOOGLEBIGTABLE™, and so forth). In some aspects, variant databasearchitectures such as column-oriented databases, in-memory databases,clustered databases, distributed databases, or even flat file datarepositories may be used according to the aspect. It will be appreciatedby one having ordinary skill in the art that any combination of known orfuture database technologies may be used as appropriate, unless aspecific database technology or a specific arrangement of components isspecified for a particular aspect described herein. Moreover, it shouldbe appreciated that the term “database” as used herein may refer to aphysical database machine, a cluster of machines acting as a singledatabase system, or a logical database within an overall databasemanagement system. Unless a specific meaning is specified for a givenuse of the term “database”, it should be construed to mean any of thesesenses of the word, all of which are understood as a plain meaning ofthe term “database” by those having ordinary skill in the art.

Similarly, some aspects may make use of one or more security systems 36and configuration systems 35. Security and configuration management arecommon information technology (IT) and web functions, and some amount ofeach are generally associated with any IT or web systems. It should beunderstood by one having ordinary skill in the art that anyconfiguration or security subsystems known in the art now or in thefuture may be used in conjunction with aspects without limitation,unless a specific security 36 or configuration system 35 or approach isspecifically required by the description of any specific aspect.

FIG. 30 shows an exemplary overview of a computer system 40 as may beused in any of the various locations throughout the system. It isexemplary of any computer that may execute code to process data. Variousmodifications and changes may be made to computer system 40 withoutdeparting from the broader scope of the system and method disclosedherein. Central processor unit (CPU) 41 is connected to bus 42, to whichbus is also connected memory 43, nonvolatile memory 44, display 47,input/output (I/O) unit 48, and network interface card (NIC) 53. I/Ounit 48 may, typically, be connected to peripherals such as a keyboard49, pointing device 50, hard disk 52, real-time clock 51, a camera 57,and other peripheral devices. NIC 53 connects to network 54, which maybe the Internet or a local network, which local network may or may nothave connections to the Internet. The system may be connected to othercomputing devices through the network via a router 55, wireless localarea network 56, or any other network connection. Also shown as part ofsystem 40 is power supply unit 45 connected, in this example, to a mainalternating current (AC) supply 46. Not shown are batteries that couldbe present, and many other devices and modifications that are well knownbut are not applicable to the specific novel functions of the currentsystem and method disclosed herein. It should be appreciated that someor all components illustrated may be combined, such as in variousintegrated applications, for example Qualcomm or Samsungsystem-on-a-chip (SOC) devices, or whenever it may be appropriate tocombine multiple capabilities or functions into a single hardware device(for instance, in mobile devices such as smartphones, video gameconsoles, in-vehicle computer systems such as navigation or multimediasystems in automobiles, or other integrated hardware devices).

In various aspects, functionality for implementing systems or methods ofvarious aspects may be distributed among any number of client and/orserver components. For example, various software modules may beimplemented for performing various functions in connection with thesystem of any particular aspect, and such modules may be variouslyimplemented to run on server and/or client components.

The skilled person will be aware of a range of possible modifications ofthe various aspects described above. Accordingly, the present inventionis defined by the claims and their equivalents.

What is claimed is:
 1. A system for thought-based control of sexualstimulation devices, comprising: a computing device comprising a memory,a processor, and a non-volatile data storage device; a database storedon the non-volatile data storage device comprising a plurality ofelectroencephalograph (EEG) training tasks, each training taskcomprising a stimulus, an objective related to a control of a sexualstimulation device, and instructions for a user to attempt to achievethe objective using a mental image or thought; an electroencephalograph(EEG) headset comprising a plurality of electrodes configured to detectelectrical activity of a human brain when the EEG headset is worn on thehead of a person and transmit EEG signal data associated with theelectrical activity; an EEG training and control application comprisinga first plurality of programming instructions stored in the memorywhich, when operating on the processor, causes the computing device to:perform EEG control training by: retrieving a first EEG training task ofthe plurality of EEG training tasks from the database; presenting thestimulus of the EEG training task and the objective of the first EEGtraining task to a person wearing the EEG headset; instructing theperson to attempt to achieve the objective using the mental image orthought according to the instructions of the first EEG training task;receiving EEG signal data from each electrode of the EEG headset whilethe person is performing the first EEG training task; identifying apattern of EEG activity from the EEG signal data; and associating theidentified EEG pattern with the objective of the first task to create anEEG pattern/objective pair; and generate thought-based control signalsfor a sexual stimulation device by: receiving the EEG signal data fromeach electrode of the EEG headset while the person is not performing thefirst EEG training task; identifying the pattern of EEG activity fromthe EEG signal data; retrieving the EEG pattern/objective pair; andgenerating a control signal for the sexual stimulation device based onthe objective of the EEG pattern/objective pair.
 2. The system of claim1, further comprising a trained machine learning algorithm comprising asecond plurality of programming instructions stored in the memory andoperating on the processor, wherein the EEG signal data is passedthrough to the trained machine learning algorithm and the trainedmachine learning algorithm performs the tasks of: identifying thepattern of EEG activity from the EEG signal data; and associating theidentified EEG pattern with the objective of the first task to createthe EEG pattern/objective pair.
 3. The system of claim 2, wherein thedatabase further comprises labeled data comprising a plurality ofadditional EEG pattern/objective pairs from other persons who haveengaged in the EEG training tasks, and wherein the machine learningalgorithm is a supervised machine learning algorithm that has beentrained on the labeled data.
 4. The system of claim 3, wherein: the EEGcontrol training is repeated, generating a plurality of EEGpattern/objective pairs for the person wearing the EEG headset; and thesupervised machine learning is retrained for the person wearing the EEGheadset using the plurality of EEG pattern/objective pairs generated forthe person.
 5. The system of claim 2, wherein the database furthercomprises unlabeled data comprising EEG signal data and task objectivesfrom other persons who have engaged in the EEG training tasks and themachine learning algorithm is an unsupervised machine learning algorithmthat has been trained on the unlabeled data.
 6. The system of claim 5,wherein: the EEG control training is repeated, generating a plurality ofEEG signal data and objectives for the person wearing the EEG headset;and the unsupervised machine learning is retrained for the personwearing the EEG headset using the plurality of EEG signal data andobjectives generated for the person.
 7. A method for thought-basedcontrol of sexual stimulation devices, comprising the steps of: storinga database stored on a non-volatile data storage device of a computingdevice comprising a memory, a processor, and the non-volatile datastorage device, the database comprising a plurality ofelectroencephalograph (EEG) training tasks, each training taskcomprising a stimulus, an objective related to a control of a sexualstimulation device, and instructions for a user to attempt to achievethe objective using a mental image or thought; using anelectroencephalograph (EEG) headset comprising a plurality of electrodesto detect electrical activity of a human brain when the EEG headset isworn on the head of a person and to transmit EEG signal data associatedwith the electrical activity to an EEG training and control applicationoperating on the computing device; and using the EEG training andcontrol application operating on the computing device to: perform EEGcontrol training by: retrieving a first EEG training task of theplurality of EEG training tasks from the database; presenting thestimulus of the first EEG training task and the objective of the firstEEG training task to a person wearing the EEG headset; instructing theperson to attempt to achieve the objective using the mental image orthought according to the instructions of the first EEG training task;receiving EEG signal data from each electrode of the EEG headset whilethe person is performing the first EEG training task; identifying apattern of EEG activity from the EEG signal data; and associating theidentified EEG pattern with the objective of the first task to create anEEG pattern/objective pair; and generate thought-based control signalsfor a sexual stimulation device by: receiving the EEG signal data fromeach electrode of the EEG headset while the person is not performing thefirst EEG training task; identifying the pattern of EEG activity fromthe EEG signal data; retrieving the EEG pattern/objective pair; andgenerating a control signal for the sexual stimulation device based onthe objective of the EEG pattern/objective pair.
 8. The method of claim7, further comprising the steps of: passing the EEG signal data throughto a trained machine learning algorithm operating on the computingdevice; and using the trained machine learning algorithm to perform thetasks of: identifying the pattern of EEG activity from the EEG signaldata; and associating the identified EEG pattern with the objective ofthe first task to create the EEG pattern/objective pair.
 9. The methodof claim 8, wherein the database further comprises labeled datacomprising a plurality of additional EEG pattern/objective pairs fromother persons who have engaged in the EEG training tasks, and whereinthe machine learning algorithm is a supervised machine learningalgorithm that has been trained on the labeled data.
 10. The method ofclaim 9, further comprising the steps of: repeating the EEG controltraining, generating a plurality of EEG pattern/objective pairs for theperson wearing the EEG headset; and retraining the supervised machinelearning for the person wearing the EEG headset using the plurality ofEEG pattern/objective pairs generated for the person.
 11. The method ofclaim 8, wherein the database further comprises unlabeled datacomprising EEG signal data and task objectives from other persons whohave engaged in the EEG training tasks and the machine learningalgorithm is an unsupervised machine learning algorithm that has beentrained on the unlabeled data.
 12. The method of claim 11, furthercomprising the steps of: repeating the EEG control training, generatinga plurality of EEG signal data and objectives for the person wearing theEEG headset; and retraining the unsupervised machine learning for theperson wearing the EEG headset using the plurality of EEG signal dataand objectives generated for the person.